import tensorflow as tf
import pickle as pk
conv2d_1 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=(2,2), padding="same", activation=None, use_bias=False)
relu_1 = tf.keras.layers.ReLU()
conv2d_2 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_2 = tf.keras.layers.ReLU()
dw_conv2d_1 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_3 = tf.keras.layers.Conv2D(filters=128, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_3 = tf.keras.layers.ReLU()
dw_conv2d_2 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_4 = tf.keras.layers.Conv2D(filters=128, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
mp2d_1 = tf.keras.layers.MaxPooling2D(pool_size=2, padding="same")
conv2d_5 = tf.keras.layers.Conv2D(filters=128, kernel_size=1, strides=(2,2), padding="same", activation=None, use_bias=False)
add_1 = tf.keras.layers.Add()
relu_4 = tf.keras.layers.ReLU()
dw_conv2d_3 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_6 = tf.keras.layers.Conv2D(filters=256, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_5 = tf.keras.layers.ReLU()
dw_conv2d_4 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_7 = tf.keras.layers.Conv2D(filters=256, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
mp2d_2 = tf.keras.layers.MaxPooling2D(pool_size=2, padding="same")
conv2d_8 = tf.keras.layers.Conv2D(filters=256, kernel_size=1, strides=(2,2), padding="same", activation=None, use_bias=False)
add_2 = tf.keras.layers.Add()
relu_6 = tf.keras.layers.ReLU()
dw_conv2d_5 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_9 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_7 = tf.keras.layers.ReLU()
dw_conv2d_6 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_10 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
mp2d_3 = tf.keras.layers.MaxPooling2D(pool_size=2, padding="same")
conv2d_11 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(2,2), padding="same", activation=None, use_bias=False)
add_3 = tf.keras.layers.Add()
relu_8 = tf.keras.layers.ReLU()
dw_conv2d_7 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_12 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_9 = tf.keras.layers.ReLU()
dw_conv2d_8 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_13 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_10 = tf.keras.layers.ReLU()
dw_conv2d_9 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_14 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_4 = tf.keras.layers.Add()
relu_11 = tf.keras.layers.ReLU()
dw_conv2d_10 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_15 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_12 = tf.keras.layers.ReLU()
dw_conv2d_11 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_16 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_13 = tf.keras.layers.ReLU()
dw_conv2d_12 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_17 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_5 = tf.keras.layers.Add()
relu_14 = tf.keras.layers.ReLU()
dw_conv2d_13 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_18 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_15 = tf.keras.layers.ReLU()
dw_conv2d_14 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_19 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_16 = tf.keras.layers.ReLU()
dw_conv2d_15 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_20 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_6 = tf.keras.layers.Add()
relu_17 = tf.keras.layers.ReLU()
dw_conv2d_16 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_21 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_18 = tf.keras.layers.ReLU()
dw_conv2d_17 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_22 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_19 = tf.keras.layers.ReLU()
dw_conv2d_18 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_23 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_7 = tf.keras.layers.Add()
relu_20 = tf.keras.layers.ReLU()
dw_conv2d_19 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_24 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_21 = tf.keras.layers.ReLU()
dw_conv2d_20 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_25 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_22 = tf.keras.layers.ReLU()
dw_conv2d_21 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_26 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_8 = tf.keras.layers.Add()
relu_23 = tf.keras.layers.ReLU()
dw_conv2d_22 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_27 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_24 = tf.keras.layers.ReLU()
dw_conv2d_23 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_28 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_25 = tf.keras.layers.ReLU()
dw_conv2d_24 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_29 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_9 = tf.keras.layers.Add()
relu_26 = tf.keras.layers.ReLU()
dw_conv2d_25 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_30 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_27 = tf.keras.layers.ReLU()
dw_conv2d_26 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_31 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_28 = tf.keras.layers.ReLU()
dw_conv2d_27 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_32 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_10 = tf.keras.layers.Add()
relu_29 = tf.keras.layers.ReLU()
dw_conv2d_28 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_33 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_30 = tf.keras.layers.ReLU()
dw_conv2d_29 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_34 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_31 = tf.keras.layers.ReLU()
dw_conv2d_30 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_35 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
add_11 = tf.keras.layers.Add()
relu_32 = tf.keras.layers.ReLU()
dw_conv2d_31 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_36 = tf.keras.layers.Conv2D(filters=728, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_33 = tf.keras.layers.ReLU()
dw_conv2d_32 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_37 = tf.keras.layers.Conv2D(filters=1024, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
mp2d_4 = tf.keras.layers.MaxPooling2D(pool_size=2, padding="same")
conv2d_38 = tf.keras.layers.Conv2D(filters=1024, kernel_size=1, strides=(2,2), padding="same", activation=None, use_bias=False)
add_12 = tf.keras.layers.Add()
dw_conv2d_33 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_39 = tf.keras.layers.Conv2D(filters=1536, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_34 = tf.keras.layers.ReLU()
dw_conv2d_34 = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=(1,1), padding="same", activation=None, depth_multiplier=1, use_bias=False)
conv2d_40 = tf.keras.layers.Conv2D(filters=2048, kernel_size=1, strides=(1,1), padding="same", activation=None, use_bias=False)
relu_35 = tf.keras.layers.ReLU()
avgp2d_1 = tf.keras.layers.AveragePooling2D(pool_size=10, padding="same")
flt_1 = tf.keras.layers.Flatten()
ds_1 = tf.keras.layers.Dense(activation=None, units=1000)
softmax_1 = tf.keras.layers.Softmax()
bn_1 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_2 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_3 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_4 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_5 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_6 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_7 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_8 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_9 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_10 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_11 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_12 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_13 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_14 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_15 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_16 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_17 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_18 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_19 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_20 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_21 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_22 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_23 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_24 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_25 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_26 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_27 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_28 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_29 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_30 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_31 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_32 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_33 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_34 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_35 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_36 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_37 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_38 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_39 = tf.keras.layers.BatchNormalization(center=True, scale=True)
bn_40 = tf.keras.layers.BatchNormalization(center=True, scale=True)
x = tf.keras.Input(shape=(299, 299, 3))
x_2 = conv2d_1(x)
x_3 = bn_1(x_2)
x_4 = relu_1(x_3)
x_5 = conv2d_2(x_4)
x_6 = bn_2(x_5)
x_7 = relu_2(x_6)
x_8 = dw_conv2d_1(x_7)
x_9 = conv2d_3(x_8)
x_10 = conv2d_5(x_7)
x_11 = bn_3(x_9)
x_12 = bn_5(x_10)
x_13 = relu_3(x_11)
x_14 = dw_conv2d_2(x_13)
x_15 = conv2d_4(x_14)
x_16 = bn_4(x_15)
x_17 = mp2d_1(x_16)
x_18 = add_1([x_12, x_17])
x_19 = relu_4(x_18)
x_20 = dw_conv2d_3(x_19)
x_21 = conv2d_6(x_20)
x_22 = conv2d_8(x_18)
x_23 = bn_6(x_21)
x_24 = bn_8(x_22)
x_25 = relu_5(x_23)
x_26 = dw_conv2d_4(x_25)
x_27 = conv2d_7(x_26)
x_28 = bn_7(x_27)
x_29 = mp2d_2(x_28)
x_30 = add_2([x_24, x_29])
x_31 = relu_6(x_30)
x_32 = dw_conv2d_5(x_31)
x_33 = conv2d_9(x_32)
x_34 = conv2d_11(x_30)
x_35 = bn_9(x_33)
x_36 = bn_11(x_34)
x_37 = relu_7(x_35)
x_38 = dw_conv2d_6(x_37)
x_39 = conv2d_10(x_38)
x_40 = bn_10(x_39)
x_41 = mp2d_3(x_40)
x_42 = add_3([x_36, x_41])
x_43 = relu_8(x_42)
x_44 = dw_conv2d_7(x_43)
x_45 = conv2d_12(x_44)
x_46 = bn_12(x_45)
x_47 = relu_9(x_46)
x_48 = dw_conv2d_8(x_47)
x_49 = conv2d_13(x_48)
x_50 = bn_13(x_49)
x_51 = relu_10(x_50)
x_52 = dw_conv2d_9(x_51)
x_53 = conv2d_14(x_52)
x_54 = bn_14(x_53)
x_55 = add_4([x_54, x_42])
x_56 = relu_11(x_55)
x_57 = dw_conv2d_10(x_56)
x_58 = conv2d_15(x_57)
x_59 = bn_15(x_58)
x_60 = relu_12(x_59)
x_61 = dw_conv2d_11(x_60)
x_62 = conv2d_16(x_61)
x_63 = bn_16(x_62)
x_64 = relu_13(x_63)
x_65 = dw_conv2d_12(x_64)
x_66 = conv2d_17(x_65)
x_67 = bn_17(x_66)
x_68 = add_5([x_67, x_55])
x_69 = relu_14(x_68)
x_70 = dw_conv2d_13(x_69)
x_71 = conv2d_18(x_70)
x_72 = bn_18(x_71)
x_73 = relu_15(x_72)
x_74 = dw_conv2d_14(x_73)
x_75 = conv2d_19(x_74)
x_76 = bn_19(x_75)
x_77 = relu_16(x_76)
x_78 = dw_conv2d_15(x_77)
x_79 = conv2d_20(x_78)
x_80 = bn_20(x_79)
x_81 = add_6([x_80, x_68])
x_82 = relu_17(x_81)
x_83 = dw_conv2d_16(x_82)
x_84 = conv2d_21(x_83)
x_85 = bn_21(x_84)
x_86 = relu_18(x_85)
x_87 = dw_conv2d_17(x_86)
x_88 = conv2d_22(x_87)
x_89 = bn_22(x_88)
x_90 = relu_19(x_89)
x_91 = dw_conv2d_18(x_90)
x_92 = conv2d_23(x_91)
x_93 = bn_23(x_92)
x_94 = add_7([x_93, x_81])
x_95 = relu_20(x_94)
x_96 = dw_conv2d_19(x_95)
x_97 = conv2d_24(x_96)
x_98 = bn_24(x_97)
x_99 = relu_21(x_98)
x_100 = dw_conv2d_20(x_99)
x_101 = conv2d_25(x_100)
x_102 = bn_25(x_101)
x_103 = relu_22(x_102)
x_104 = dw_conv2d_21(x_103)
x_105 = conv2d_26(x_104)
x_106 = bn_26(x_105)
x_107 = add_8([x_106, x_94])
x_108 = relu_23(x_107)
x_109 = dw_conv2d_22(x_108)
x_110 = conv2d_27(x_109)
x_111 = bn_27(x_110)
x_112 = relu_24(x_111)
x_113 = dw_conv2d_23(x_112)
x_114 = conv2d_28(x_113)
x_115 = bn_28(x_114)
x_116 = relu_25(x_115)
x_117 = dw_conv2d_24(x_116)
x_118 = conv2d_29(x_117)
x_119 = bn_29(x_118)
x_120 = add_9([x_119, x_107])
x_121 = relu_26(x_120)
x_122 = dw_conv2d_25(x_121)
x_123 = conv2d_30(x_122)
x_124 = bn_30(x_123)
x_125 = relu_27(x_124)
x_126 = dw_conv2d_26(x_125)
x_127 = conv2d_31(x_126)
x_128 = bn_31(x_127)
x_129 = relu_28(x_128)
x_130 = dw_conv2d_27(x_129)
x_131 = conv2d_32(x_130)
x_132 = bn_32(x_131)
x_133 = add_10([x_132, x_120])
x_134 = relu_29(x_133)
x_135 = dw_conv2d_28(x_134)
x_136 = conv2d_33(x_135)
x_137 = bn_33(x_136)
x_138 = relu_30(x_137)
x_139 = dw_conv2d_29(x_138)
x_140 = conv2d_34(x_139)
x_141 = bn_34(x_140)
x_142 = relu_31(x_141)
x_143 = dw_conv2d_30(x_142)
x_144 = conv2d_35(x_143)
x_145 = bn_35(x_144)
x_146 = add_11([x_145, x_133])
x_147 = relu_32(x_146)
x_148 = dw_conv2d_31(x_147)
x_149 = conv2d_36(x_148)
x_150 = conv2d_38(x_146)
x_151 = bn_36(x_149)
x_152 = bn_38(x_150)
x_153 = relu_33(x_151)
x_154 = dw_conv2d_32(x_153)
x_155 = conv2d_37(x_154)
x_156 = bn_37(x_155)
x_157 = mp2d_4(x_156)
x_158 = add_12([x_152, x_157])
x_159 = dw_conv2d_33(x_158)
x_160 = conv2d_39(x_159)
x_161 = bn_39(x_160)
x_162 = relu_34(x_161)
x_163 = dw_conv2d_34(x_162)
x_164 = conv2d_40(x_163)
x_165 = bn_40(x_164)
x_166 = relu_35(x_165)
x_167 = avgp2d_1(x_166)
x_168 = flt_1(x_167)
x_169 = ds_1(x_168)
x_170 = softmax_1(x_169)
model = tf.keras.Model(inputs=x, outputs=x_170)
weight_data = pk.load(open("tmp_weight.pkl", "rb"))
conv2d_1.set_weights([weight_data["p0"]])
conv2d_2.set_weights([weight_data["p7"]])
dw_conv2d_1.set_weights([weight_data["p14"]])
conv2d_3.set_weights([weight_data["p15"]])
dw_conv2d_2.set_weights([weight_data["p22"]])
conv2d_4.set_weights([weight_data["p23"]])
conv2d_5.set_weights([weight_data["p30"]])
dw_conv2d_3.set_weights([weight_data["p37"]])
conv2d_6.set_weights([weight_data["p38"]])
dw_conv2d_4.set_weights([weight_data["p45"]])
conv2d_7.set_weights([weight_data["p46"]])
conv2d_8.set_weights([weight_data["p53"]])
dw_conv2d_5.set_weights([weight_data["p60"]])
conv2d_9.set_weights([weight_data["p61"]])
dw_conv2d_6.set_weights([weight_data["p68"]])
conv2d_10.set_weights([weight_data["p69"]])
conv2d_11.set_weights([weight_data["p76"]])
dw_conv2d_7.set_weights([weight_data["p83"]])
conv2d_12.set_weights([weight_data["p84"]])
dw_conv2d_8.set_weights([weight_data["p91"]])
conv2d_13.set_weights([weight_data["p92"]])
dw_conv2d_9.set_weights([weight_data["p99"]])
conv2d_14.set_weights([weight_data["p100"]])
dw_conv2d_10.set_weights([weight_data["p107"]])
conv2d_15.set_weights([weight_data["p108"]])
dw_conv2d_11.set_weights([weight_data["p115"]])
conv2d_16.set_weights([weight_data["p116"]])
dw_conv2d_12.set_weights([weight_data["p123"]])
conv2d_17.set_weights([weight_data["p124"]])
dw_conv2d_13.set_weights([weight_data["p131"]])
conv2d_18.set_weights([weight_data["p132"]])
dw_conv2d_14.set_weights([weight_data["p139"]])
conv2d_19.set_weights([weight_data["p140"]])
dw_conv2d_15.set_weights([weight_data["p147"]])
conv2d_20.set_weights([weight_data["p148"]])
dw_conv2d_16.set_weights([weight_data["p155"]])
conv2d_21.set_weights([weight_data["p156"]])
dw_conv2d_17.set_weights([weight_data["p163"]])
conv2d_22.set_weights([weight_data["p164"]])
dw_conv2d_18.set_weights([weight_data["p171"]])
conv2d_23.set_weights([weight_data["p172"]])
dw_conv2d_19.set_weights([weight_data["p179"]])
conv2d_24.set_weights([weight_data["p180"]])
dw_conv2d_20.set_weights([weight_data["p187"]])
conv2d_25.set_weights([weight_data["p188"]])
dw_conv2d_21.set_weights([weight_data["p195"]])
conv2d_26.set_weights([weight_data["p196"]])
dw_conv2d_22.set_weights([weight_data["p203"]])
conv2d_27.set_weights([weight_data["p204"]])
dw_conv2d_23.set_weights([weight_data["p211"]])
conv2d_28.set_weights([weight_data["p212"]])
dw_conv2d_24.set_weights([weight_data["p219"]])
conv2d_29.set_weights([weight_data["p220"]])
dw_conv2d_25.set_weights([weight_data["p227"]])
conv2d_30.set_weights([weight_data["p228"]])
dw_conv2d_26.set_weights([weight_data["p235"]])
conv2d_31.set_weights([weight_data["p236"]])
dw_conv2d_27.set_weights([weight_data["p243"]])
conv2d_32.set_weights([weight_data["p244"]])
dw_conv2d_28.set_weights([weight_data["p251"]])
conv2d_33.set_weights([weight_data["p252"]])
dw_conv2d_29.set_weights([weight_data["p259"]])
conv2d_34.set_weights([weight_data["p260"]])
dw_conv2d_30.set_weights([weight_data["p267"]])
conv2d_35.set_weights([weight_data["p268"]])
dw_conv2d_31.set_weights([weight_data["p275"]])
conv2d_36.set_weights([weight_data["p276"]])
dw_conv2d_32.set_weights([weight_data["p283"]])
conv2d_37.set_weights([weight_data["p284"]])
conv2d_38.set_weights([weight_data["p291"]])
dw_conv2d_33.set_weights([weight_data["p298"]])
conv2d_39.set_weights([weight_data["p299"]])
dw_conv2d_34.set_weights([weight_data["p306"]])
conv2d_40.set_weights([weight_data["p307"]])
ds_1.set_weights([weight_data["p314"].T, weight_data["p315"]])
bn_1.set_weights([weight_data["p4"], weight_data["p6"], weight_data["p5"], weight_data["p2"]])
bn_2.set_weights([weight_data["p11"], weight_data["p13"], weight_data["p12"], weight_data["p9"]])
bn_3.set_weights([weight_data["p19"], weight_data["p21"], weight_data["p20"], weight_data["p17"]])
bn_4.set_weights([weight_data["p27"], weight_data["p29"], weight_data["p28"], weight_data["p25"]])
bn_5.set_weights([weight_data["p34"], weight_data["p36"], weight_data["p35"], weight_data["p32"]])
bn_6.set_weights([weight_data["p42"], weight_data["p44"], weight_data["p43"], weight_data["p40"]])
bn_7.set_weights([weight_data["p50"], weight_data["p52"], weight_data["p51"], weight_data["p48"]])
bn_8.set_weights([weight_data["p57"], weight_data["p59"], weight_data["p58"], weight_data["p55"]])
bn_9.set_weights([weight_data["p65"], weight_data["p67"], weight_data["p66"], weight_data["p63"]])
bn_10.set_weights([weight_data["p73"], weight_data["p75"], weight_data["p74"], weight_data["p71"]])
bn_11.set_weights([weight_data["p80"], weight_data["p82"], weight_data["p81"], weight_data["p78"]])
bn_12.set_weights([weight_data["p88"], weight_data["p90"], weight_data["p89"], weight_data["p86"]])
bn_13.set_weights([weight_data["p96"], weight_data["p98"], weight_data["p97"], weight_data["p94"]])
bn_14.set_weights([weight_data["p104"], weight_data["p106"], weight_data["p105"], weight_data["p102"]])
bn_15.set_weights([weight_data["p112"], weight_data["p114"], weight_data["p113"], weight_data["p110"]])
bn_16.set_weights([weight_data["p120"], weight_data["p122"], weight_data["p121"], weight_data["p118"]])
bn_17.set_weights([weight_data["p128"], weight_data["p130"], weight_data["p129"], weight_data["p126"]])
bn_18.set_weights([weight_data["p136"], weight_data["p138"], weight_data["p137"], weight_data["p134"]])
bn_19.set_weights([weight_data["p144"], weight_data["p146"], weight_data["p145"], weight_data["p142"]])
bn_20.set_weights([weight_data["p152"], weight_data["p154"], weight_data["p153"], weight_data["p150"]])
bn_21.set_weights([weight_data["p160"], weight_data["p162"], weight_data["p161"], weight_data["p158"]])
bn_22.set_weights([weight_data["p168"], weight_data["p170"], weight_data["p169"], weight_data["p166"]])
bn_23.set_weights([weight_data["p176"], weight_data["p178"], weight_data["p177"], weight_data["p174"]])
bn_24.set_weights([weight_data["p184"], weight_data["p186"], weight_data["p185"], weight_data["p182"]])
bn_25.set_weights([weight_data["p192"], weight_data["p194"], weight_data["p193"], weight_data["p190"]])
bn_26.set_weights([weight_data["p200"], weight_data["p202"], weight_data["p201"], weight_data["p198"]])
bn_27.set_weights([weight_data["p208"], weight_data["p210"], weight_data["p209"], weight_data["p206"]])
bn_28.set_weights([weight_data["p216"], weight_data["p218"], weight_data["p217"], weight_data["p214"]])
bn_29.set_weights([weight_data["p224"], weight_data["p226"], weight_data["p225"], weight_data["p222"]])
bn_30.set_weights([weight_data["p232"], weight_data["p234"], weight_data["p233"], weight_data["p230"]])
bn_31.set_weights([weight_data["p240"], weight_data["p242"], weight_data["p241"], weight_data["p238"]])
bn_32.set_weights([weight_data["p248"], weight_data["p250"], weight_data["p249"], weight_data["p246"]])
bn_33.set_weights([weight_data["p256"], weight_data["p258"], weight_data["p257"], weight_data["p254"]])
bn_34.set_weights([weight_data["p264"], weight_data["p266"], weight_data["p265"], weight_data["p262"]])
bn_35.set_weights([weight_data["p272"], weight_data["p274"], weight_data["p273"], weight_data["p270"]])
bn_36.set_weights([weight_data["p280"], weight_data["p282"], weight_data["p281"], weight_data["p278"]])
bn_37.set_weights([weight_data["p288"], weight_data["p290"], weight_data["p289"], weight_data["p286"]])
bn_38.set_weights([weight_data["p295"], weight_data["p297"], weight_data["p296"], weight_data["p293"]])
bn_39.set_weights([weight_data["p303"], weight_data["p305"], weight_data["p304"], weight_data["p301"]])
bn_40.set_weights([weight_data["p311"], weight_data["p313"], weight_data["p312"], weight_data["p309"]])
